Author archive - marc.carreras

About the Author
Author

marc.carreras

Persistent Valve Turning

On December 2014, UdG team together with KCL, IIT and NTUA conducted experiments about persistent autonomy in the context of the valve turning scenario. We have combined different disciplines involving: visual-based detection and localization systems, robust control schemes, learning-by-demonstration techniques for intervention and a temporal planning architecture, to operate many times a valve panel mock-up in front of several disturbances: water currents, blocked valves, panel unknown position and panel occlusion. All these systems and methodologies have been validated individually and in an integrated long term experiment, in which the AUV locates an intervention panel and performs multiple valve turning operations while handling several failures (either spontaneous or induced) that take place along the mission time. Experiments have only been performed in water tank, in which exhaustive experiments were possible testing differents conditions and configurations. The positive results encourage the use of, in the short-term, autonomous robots operating in subsea facilities performing interventions with a cost benefit, in comparison with teleoperated vehicles.

The long-term mission was carried out in a water tank of 16x8x5 meters using the Girona 500 AUV equipped with a 4 Degrees of Freedom electrical manipulator, a Stereo Camera and a specifically designed end-effector, which had a camera in-hand and force and torque sensor. A mock-up panel of 0.8×0.5 meters with 4 valve handles was used to emulate a subsea panel from the offshore industry. To make the environment more realistic, two propellers, able to generate up to 14Kg of thrust each, were placed close to the panel in order to generate lateral water currents. Experiments were performed in a completely autonomous mode, the vehicle ran on its own batteries and all required processing was performed with the on-board computers.

Girona 500 AUV with the valve turning payload in the water tank, the valve panel mock-up and the external propellers for water current perturbation.

Girona 500 AUV with the valve turning payload in the water tank, the valve panel mock-up and the external propellers for water current perturbation.

In the experiment, the vehicle had to locate the intervention panel among different locations and modify the valve handles to achieve different panel configurations. The planning algorithm generated the inspection points to locate the panel, which was located by a vision-based detection system. The vision system was also determining the state of the valve handles, which was used by the planning system to generate the actions to turn the valves and to achieve the desired panel configuration. In the process of turning a valve, several systems were working: vision system for panel and valve detection; robust controller for vehicle and manipulator control; Learning By Demonstration for moving the AUV and the manipulator; reactive fuzzy decision maker for deciding if the task could be completed; and Force and Torque sensor processing for determining the contact and turning of the valves. After attempting a valve turning, the planning checked again the state of the valves, and decided new actions in case the action failed due to perturbations. During more than 3 hours, the AUV changed the panel to 9 different configurations, which required the turning of 29 valves. In order to accomplish these valve turnings, the planner generated 37 valve turning actions: 23 were successful; 10 failed because the valve was blocked; and 4 failed because the platform could not execute the action due to the high water current perturbations.

This table summarizes the results of the long-term experiment which tried to achieve 9 configurations, with 37 valve turning attempts. Some of them were accomplished (23) and the rest were not accomplished due to the fact that the valve was blocked (10) or because the platform could not face the perturbations (4).

This table summarizes the results of the long-term experiment which tried to achieve 9 configurations, with 37 valve turning attempts. Some of them were accomplished (23) and the rest were not accomplished due to the fact that the valve was blocked (10) or because the platform could not face the perturbations (4).

Watch the long-term autonomous intervention:

Chain inspection at sea

On January 2015 the UdG team performed some experiments at sea for testing the final developments regarding chain inspection. We used a high resolution imaging sonar, which delivers acoustic images at near-video frame rate, in order to detect each of the links and follow the chain. In this way, the system can operate regardless of the visibility conditions and the suspended marine fouling that may arise during cleaning. However working with sonar data introduces several challenges (noisy data, insonification artifacts, narrow field of view, etc.) that had to be addressed. We have tackled the problem in two different configurations: a chain lying on the seafloor and a chain suspended vertically in the water column. For each of these configurations we have provided solutions for chain detection and for chain following using forward-looking sonar and also multibeam data in the vertical case.

Mock-up of the chain lying on the seafloor (left) and hanging vertically (middle and right), in Sant Feliu de Guixols (Girona coast). The water visibility was very reduced pointing out the benefit of using acoustic sensors.

Mock-up of the chain lying on the seafloor (left) and hanging vertically (middle and right), in Sant Feliu de Guixols (Girona coast). The water visibility was very reduced pointing out the benefit of using acoustic sensors.

After successful performance of the chain detection and following algorithms in the water tank, we attempted the same procedures at sea, thus performing a final demonstration one step closer to a real operational environment, and exposing the system to more challenging conditions (larger environment, worse visibility, water currents, etc). Finally, we have also developed a system for forward-looking sonar mapping to perform a first evaluation of the chain state at a high level. This allows seeing an overall view of the spatial layout of the links in the environment as well as provides a map of increased signal-to-noise ratio with respect to the individual frames in which features on the range of few centimeters can be appreciated.

mosaic

Search trajectory and inspection of the horizontal chain at sea. After following several waypoints, Girona 500 AUV found the links of the chain and started the inspection. Left image shows the trajectory of the AUV and right image shows the post processed acoustic mosaic of the same trajectory.

Inspection of the chain in vertical position at sea. The left image shows the point cloud representation of the chain acquired with the acoustic multibeam. The right image shows the post-processed acoustic mosaic of the chain acquired with the forward looking sonar.

Inspection of the chain in vertical position at sea. The left image shows the point cloud representation of the chain acquired with the acoustic multibeam. The right image shows the post-processed acoustic mosaic of the chain acquired with the forward looking sonar.

Watch the horizontal chain inspection:

Watch the vertical chain inspection: